HDU-1087-Super Jumping! Jumping! Jumping!(dp)

本文介绍了一款名为“SuperJumping”的棋盘游戏,并详细解析了如何通过算法找到玩家获得最高分数的策略。该算法利用动态规划求解最大递增子序列问题,最终输出给定棋子列表下所能取得的最大值。

Super Jumping! Jumping! Jumping!


Problem Description
Nowadays, a kind of chess game called “Super Jumping! Jumping! Jumping!” is very popular in HDU. Maybe you are a good boy, and know little about this game, so I introduce it to you now.



The game can be played by two or more than two players. It consists of a chessboard(棋盘)and some chessmen(棋子), and all chessmen are marked by a positive integer or “start” or “end”. The player starts from start-point and must jumps into end-point finally. In the course of jumping, the player will visit the chessmen in the path, but everyone must jumps from one chessman to another absolutely bigger (you can assume start-point is a minimum and end-point is a maximum.). And all players cannot go backwards. One jumping can go from a chessman to next, also can go across many chessmen, and even you can straightly get to end-point from start-point. Of course you get zero point in this situation. A player is a winner if and only if he can get a bigger score according to his jumping solution. Note that your score comes from the sum of value on the chessmen in you jumping path.
Your task is to output the maximum value according to the given chessmen list.
 

Input
Input contains multiple test cases. Each test case is described in a line as follow:
N value_1 value_2 …value_N
It is guarantied that N is not more than 1000 and all value_i are in the range of 32-int.
A test case starting with 0 terminates the input and this test case is not to be processed.
 

Output
For each case, print the maximum according to rules, and one line one case.
 

Sample Input
3 1 3 24 1 2 3 44 3 3 2 10
 
Sample Output
4103

#include<bits/stdc++.h>
using namespace std;
int a[1005];
int dp[1005];///当处于编号为i时的最大值
int main()
{
    int n,sum,k,Max;
    while(1)
    {
        Max=0;
        scanf("%d",&n);
        if(n==0)break;
        for(int i=0;i<n;i++)
        {
            scanf("%d",&a[i]);
            dp[i]=a[i];///初始化dp数组,使数组初始时为本身,因比较的是当前最大价值和dp[j]的最大价值加上a[i];
        }
        for(int i=0;i<n;i++)///后
        {
            for(int j=0;j<i;j++)///前
            {
                if(a[i]>a[j])///如果后面的值大于前面的值
                dp[i]=max(dp[i],dp[j]+a[i]);///当前最大价值和dp[j]的最大价值加上a[i];
            }
            Max=max(dp[i],Max);
        }
        printf("%d\n",Max);
    }
    return 0;
}
求最大递增子序列,找到每一个点时的最大值,从所以点中找最大值,即为结果。第一个循环为每一个点,也就是后面的那个数所在的位置的dp[i],和前面的数j的dp[j]+a[i]相比较。结果为,当前位置能达到的最大值。

内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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